Triple
T5493721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British Rail Class 158 |
E123762
|
entity |
| Predicate | numberOfCarsPerSet |
P64438
|
FINISHED |
| Object | 2 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 2 | Statement: [British Rail Class 158, numberOfCarsPerSet, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCarsPerSet Context triple: [British Rail Class 158, numberOfCarsPerSet, 2]
-
A.
numberOfCarsPerUnit
Indicates the quantity of cars associated with each single unit of a specified measure (such as time, distance, or entity).
-
B.
numberOfPassengerCars
Indicates the total count of passenger cars associated with or contained in a given entity or context.
-
C.
rowsPerCar
Indicates the number of rows associated with or allocated to each individual car.
-
D.
numberOfVehicles
Indicates the total count of vehicles associated with a given entity or context.
-
E.
numberOfPowerCars
Indicates the relationship specifying how many power cars (self-propelled units) are associated with or contained in a given train or rail consist.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd464a2d908190869324ce176779c8 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd9281a0148190bb7a8dae9c991b9c |
completed | March 20, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69bd91a8df6481908d1643f7342fe6f0 |
completed | March 20, 2026, 6:27 p.m. |
| PDg | Predicate description generation | batch_69bd925c62a88190ac932444d5170bdd |
completed | March 20, 2026, 6:30 p.m. |
Created at: March 20, 2026, 2:10 p.m.